Summary of Griffon V2: Advancing Multimodal Perception with High-resolution Scaling and Visual-language Co-referring, by Yufei Zhan et al.
Griffon v2: Advancing Multimodal Perception with High-Resolution Scaling and Visual-Language Co-Referring
by Yufei Zhan, Yousong Zhu, Hongyin Zhao, Fan Yang, Ming Tang, Jinqiao Wang
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a unified high-resolution generalist model, Griffon v2, which enables flexible object referring with visual and textual prompts. The model addresses the limitation of image resolution in Large Vision Language Models, allowing it to achieve nuanced visual and language referring in domains such as GUI Agents, Counting, etc. To efficiently scale up image resolution, a simple and lightweight down-sampling projector is designed, preserving complete contexts and fine details. The model also incorporates visual-language co-referring capabilities through a plug-and-play visual tokenizer, enabling user-friendly interaction with flexible target images, free-form texts, and even coordinates. Experimental results demonstrate that Griffon v2 achieves state-of-the-art performance on REC, phrase grounding, and REG tasks, outperforming expert models in object detection and object counting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new model called Griffon v2 that can understand pictures and words together. This helps it do tasks like finding objects, understanding what’s being said about those objects, and even counting them. The model is special because it can work with high-resolution images, which means it can see lots of details. It also has something called a “visual tokenizer” that lets it talk to users in a more natural way, using words and pictures together. |
Keywords
» Artificial intelligence » Grounding » Object detection » Tokenizer
